Telepresence Robots in Long-Term Care Settings in British Columbia During the COVID-19 Pandemic: Analyzing the Experiences of Residents and Family Members
Bibliographic record
Abstract
Telepresence robots are being increasingly used in long-term care (LTC) homes for virtual visits between families and residents. This study investigated the impacts and experiences of residents and their families using a telepresence robot (Double 3) for 4 to 12 months during the COVID-19 pandemic. A total of 51 participants were recruited through purposive sampling, including 9 residents, 15 family members, and 27 staff from 4 LTC homes in British Columbia, Canada. Conversational interviews were conducted with residents and family members. Focus groups and one-to-one interviews were held with frontline staff. Thematic analysis was performed, with five themes identified: (1) Stay connected, (2) Regain autonomy, (3) Relieve caregiver burden, (4) Environmental and technical issues, and (5) Scheduling concerns. In conclusion, telepresence robots helped maintain social connections between residents and families, despite the COVID-19 restrictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".